Feb 10-11, 2016

R is a versatile, open source programming/scripting language that's useful for statistics but also any data-oriented tasks (including plotting).

• Open source software under GPL.
  
• Superior (if not just comparable) to commercial alternatives.
• Over 5,000 user contributed packages at this time.
• Widely used both in academia and industry.
  
• Available on all platforms.
• Large and growing community of peers.

It will make you more efficient and your work more repeatable and reliable

• Introduction to R and RStudio, and some of the fundamentals of R
• Getting acquainted with some of the basics of working with data in R
• Data Visualization (plotting)
• Data "Munging" (manipulation)
• Performing basic statistical analyses
• Repeating things
• Writing Functions
• Best Practices
• How to get help

• Its really important that you know what you did.
• A lot of scientific work is not reproducible.
• If you keep a lab or field notebook, why are we not as careful with our data?
• We edit each others manuscripts, but we don't edit each other's code.
• If you write your code with "future you" in mind, you will save yourself and others a lot of time.
• Code and workflow is more reproducible if we can document everything that we do.
• Our end goal is not just to "do stuff" but to do it in a way that anyone can easily and exactly replicate our workflow and results.

From highest to lowest precedence:

• Parentheses: (, )
• Exponents: ^
• Divide: /
• Multiply: *
• Add: +
• Subtract: -

1 == 1  # equality (note two equals signs, read as "is equal to")

1 != 2  # inequality (read as "is not equal to")

1 <  2  # less than

1 <= 1  # less than or equal to

1 > 0  # greater than

1 >= -9 # greater than or equal to

Most of R's power and flexibility comes from functions.

A function is a saved object that takes inputs to perform a task.

Functions take in information and return outputs.

A function takes zero, one, or many arguments (also called parameters), depending on the function, and returns a value.

To call a function, type its name followed by brackets (). Arguments go inside the brackets and are separated by commas.

name_of_function(arg1,arg2,arg3)

Projects - keeping things organized

• Keep all your project files in one self-contained, well-organized directory

project
├── R
│   └── functions.R
├── data
│   ├── data1.csv
│   └── data2.csv
├── doc
│   └── manuscript.doc
├── out
│   └── summaries.csv
├── plots
│   ├── plot1.png
│   └── plot2.png
├── 01-load_clean_data.R
├── 02-analysis.R
├── 03-plotting.R
└── my_project.Rproj

"There are only two hard things in Computer Science: cache invalidation and naming things." – Phil Karlton

• Name things so that you can understand them later
• Try to balance brevity with clarity: x is easy to type, but may not mean much.
• Only begin names with letters.
• Separate words with a dot (my.var) or an underscore (my_var), or use camelCase.
• Try to be consistent.
• Avoid giving things names that already exist (like mean, sum, log)

• character: "a", "swc"
• numeric: 2, 15.5
• integer: 2L (the L tells R to store this as an integer)
• logical: TRUE, FALSE
• complex: 1+4i (complex numbers with real and imaginary parts)

Grammer of Graphics:

• data
• aesthetics
• map varirables to visual representation
• x, y, colour, size, etc.
• geom layer(s)
• optional
• stat
• facets

Access a single column:

mydf$col_name

Subset rows and columns:

my_df[rows, columns]

rows and columns can be integers, character, or logical

dplyr implements the following verbs useful for data manipulation:

• group_by(): set the grouping variable(s)
• filter(): focus on a subset of rows
• select(): focus on a subset of variables
• mutate(): add new columns (also mutate_each())
• summarize(): reduce each group to a single row of summary statistics
• arrange(): re-order the rows